System and method for data recording and analysis

ABSTRACT

A system and apparatus for data recording and analyzing operational data and methods for making and using the same are disclosed. The apparatus can monitor and record data generated by a plurality of operational and extended sensors each positioned on a moving platform. The data recording and analysis system can analyze the sensor data during movement and, by performing a statistical analysis of the operational data, can advantageously adjust one or more selected performance capabilities of the platform. For example, the performance envelope of a platform can be increased or decreased according to the experience of an operator. The recorded data can be transmitted at any suitable time, including during and/or after travel. The apparatus provides redundant storage capability and the ability to store information on removable media to enable sharing of data. Thereby, the system, apparatus and method advantageously can optimize the operator&#39;s overall experience controlling a platform.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of, and claims priority to,co-pending PCT Patent Application Number PCT/CN2014/088050, which wasfiled on Sep. 30, 2014. The disclosure of the PCT application is hereinincorporated by reference in its entirety and for all purposes.

COPYRIGHT NOTICE

A portion of the disclosure of this patent document contains materialwhich is subject to copyright protection. The copyright owner has noobjection to the facsimile reproduction by anyone of the patent documentor the patent disclosure, as it appears in the Patent and TrademarkOffice patent file or records, but otherwise reserves all copyrightrights whatsoever.

FIELD

The disclosed embodiments relate generally to data recording andanalysis systems and more particularly, but not exclusively, to a systemand method for recording and analysis of operational data recorded fromsensors positioned on moving platforms.

BACKGROUND

Many civilian unmanned aerial vehicles (UAVs) do not have a flight datarecording capability, commonly known as a “black box,” for the in-flightrecording of UAV sensors for accident reconstruction. The few civilianUAVs that have a flight data recorder are relatively simple and suchsystems do not compile statistics regarding usage habits and preferredconfiguration information for an operator. Further, currently availableflight data recording systems do not utilize the operator's flightrecord to adjust the capabilities of the UAV, thus allowing varyingperformance characteristics based on different levels of operatorexperience. Also, current systems do not permit the sharing of flightdata using location-based social networks. Finally, civilian UAVs do notroutinely store images or video of flight accidents as they occur,therefore allowing for improved accident investigations.

Accordingly, there is a need for a system, method and apparatus forflight data recording and analysis of UAV flight data in order tooptimize an operator's flight experience and improve safety.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is an exemplary top-level flowchart illustrating one embodimentof a method for analyzing platform operational data and updating anoperating parameter of the platform in response to results of theplatform data analysis.

FIG. 2 is an exemplary flowchart illustrating an alternative embodimentof the method of FIG. 1, wherein the operational data analysis comprisesestablishing an operator profile and an operating parameter.

FIG. 3 is an exemplary flowchart illustrating another alternativeembodiment of the method of FIG. 1, wherein the operational dataanalysis comprises comparing operational sensor data with a modifiedoperating parameter.

FIG. 4 is an exemplary flowchart illustrating still another alternativeembodiment of the method of FIG. 1, wherein the updating an operatingparameter in response to data analysis results comprises characterizingan operator's experience based on collected event data.

FIG. 5 is an exemplary flowchart illustrating another alternativeembodiment of the method of FIG. 1, wherein the recording the platformdata is illustrated.

FIG. 6 is an exemplary flowchart illustrating another alternativeembodiment of the method of FIG. 1, wherein the transferring operationaldata to a ground based apparatus is illustrated.

FIG. 7 is an exemplary top-level diagram illustrating an embodiment of adata recording and analysis apparatus for performing the method of FIG.1.

FIG. 8 is an exemplary top-level diagram illustrating an alternativeembodiment of the apparatus of FIG. 7, wherein the apparatus furthercomprises a data storage unit.

FIG. 9 is an exemplary top-level diagram illustrating anotheralternative embodiment of the apparatus of FIG. 7, wherein the apparatusis positioned within a data recording and analysis system.

FIG. 10 is an exemplary top-level diagram illustrating a data recordingand analysis system that incorporates the apparatus of FIG. 7, whereinthe system comprises an expanded data port for communicating with aremovable data storage card.

FIG. 11 is an exemplary top-level diagram illustrating anotheralternative embodiment of the system of FIG. 10, wherein the system cancommunicate with selected ground elements.

FIG. 12 is an exemplary top-level diagram illustrating still anotheralternative embodiment of the system of FIG. 10, wherein the system isadapted for use with an unmanned aerial vehicle.

FIG. 13 is an illustration of an exemplary quadcopter unmanned aerialvehicle that can be used for the method of FIG. 1.

It should be noted that the figures are not drawn to scale and thatelements of similar structures or functions are generally represented bylike reference numerals for illustrative purposes throughout thefigures. It also should be noted that the figures are only intended tofacilitate the description of the preferred embodiments. The figures donot illustrate every aspect of the described embodiments and do notlimit the scope of the present disclosure.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

Since currently-available data recorders are incapable of performingin-flight analysis of operational data of a moving platform, a systemand method for performing data recording and analysis are disclosed. Thesystem and method advantageously can be applied with relation to a widerange of moving platforms. Exemplary moving the platform can comprise anaircraft, an automobile, a cruise ship, and/or a train locomotive. Inanother alternative embodiment, the platform can be an unmanned aerialvehicle (UAV).

Turning to FIG. 1, for example, one embodiment of a method 100 for datarecording and analysis is shown as comprising, at 110, analyzingoperational sensor data and, at 120, updating an operating parameter inresponse to results of the sensor data analysis. Updating can includechanging, altering, or maintaining the parameter.

The method 100 advantageously can be performed with relation to a widerange of moving platforms 200 (shown in FIG. 11). In an alternativeembodiment of the disclosed method 100, the platform 200 can be anaircraft, an automobile, a cruise ship, and/or a train locomotive. Inanother alternative embodiment, the platform 200 can be an unmannedaerial vehicle (UAV) 200A (shown in FIG. 12).

The operational sensor data analyzed, at 110, can comprise basicoperational data for the platform 200. Exemplary operational sensor datacan comprise current attitude, inertial measurement unit (IMU) data,power level, controller input data, controller commands such as latitudeand longitude of navigation points, altitude, speed, heading and degreeof angular rotation and/or custom-defined expanded data such as visualand non-visual data for the platform 200 without limitation.

The sensor data can be collected from various sensors 210, 220, 230(shown in FIG. 11), positioned on the platform 200. These sensors 210,220, 230 can comprise, but are not limited to, altitude, acceleration(pitch, roll, and yaw), attitude geographic position, speed, outside airtemperature, and/or barometric pressure of the platform 200.

In one embodiment, each sensor 210, 220, 230 has a predeterminedsampling rate that may be uniform and/or different from the samplingrates of other sensors 210, 220, 230 positioned on the platform 200. Thesampling rates for selected from all the sensors 210, 220, 230 canchange under selected conditions such as a rapid descent or change inacceleration. The number and type of sensors 210, 220, 230 that can bemonitored can vary from platform 200 to platform 200, and differentconfigurations of the sensors 210, 220, 230 can be positioned aboard thesame type of platform 200.

In one embodiment of the method 100, the collected sensor data can betransmitted as electrical signals from the sensors 210, 220, 230 to adata processing and analysis unit 242 (shown in FIG. 10). The dataprocessing and analysis unit 242 can save the sensor data in memory asraw data and/or filter the sensor data before being analyzed. The rawdata and/or filtered data can be saved in a data recorder 240 (shown inFIG. 11) prior to, concurrently with, or subsequent to the analysis. Insome embodiments, the sensors 210, 220, 230 can filter the sensor datainternally prior to transmission for data analysis. In some embodiments,the sensors 210, 220, 230 can internally buffer sensor data for latertransmission.

In one embodiment, the method 100 can comprise predetermining a set ofoperating parameters to be analyzed. The data analysis compares theoperational data captured from the sensors 210, 220, 230 with thepredetermined set of operating parameters. If the operational data isdetermined to be outside the established parameters, the operatingparameters can be modified. The predetermined set of operatingparameters can comprise a single parameter as a minimum number ofparameters, or a maximum number of parameters up to the processingcapabilities of the data processing and analysis unit 242.

The data analysis can be accomplished using any conventional manner forstatistical analysis. Data analysis is a process of inspecting,cleaning, transforming, and/or modeling data with a goal of discoveringuseful information, suggesting conclusions, and supporting decisionmaking. Here, the data analysis can comprise learning the habits,experience, and/or skills of a platform operator and/or to modify theperformance of the platform 200 to make operation of the platform 200safer.

In one embodiment, the analysis results can be stored in memory and, asdesired, recalled subsequently for event analysis such as accidentreconstruction.

Updating an operational parameter in response to data analysis results,at 120, can be performed in any conventional manner. One exemplarymanner comprises updating only a selected operating parameter that isoutside the established parameters. For example, if a platform 200exceeds an established velocity parameter when the platform 200 is inclose proximity to the ground, the method 100 can limit the maximumvelocity of the platform 200 for all altitudes.

In some embodiments, all operational data is analyzed. In otherembodiments, only a selected subset of operational data is analyzed. Insome embodiments, the subset of sensor data is defined over apredetermined period of time and/or over a discrete number of events.

Another manner for updating operating parameter in response to dataanalysis results, at 120, comprises modifying a combination of operatingparameters concurrently. Using the same example, if an operator isoperating a platform 200 at high airspeed in close proximity to theground, the method 100 can limit both maximum velocity and minimumaltitude (over certain speeds).

Another approach comprises monitoring several operating parameters.Based on the analysis results, a selected operating parameter may bealtered only if outside other operating limits. For example, using thisapproach the airspeed limit would be applied if the platform 200 isbelow some minimum altitude. The airspeed limit would have no effect athigher altitudes.

The method 100 can employ any of these manners individually or in anypreselected combination. The method 100 can also vary based on thesensor parameters being monitored as determined by the programming ofthe platform 200. While FIG. 1 illustrates the method 100 as twodistinct operations, the method 100 can be performed in any number ofoperations. The method 100 can collect and analyze sensor data from amultitude of different sensors 210, 220, 230. Further, the sensors 210,220, 230 can transmit the sensor data in any conventional wired and/orwireless manner. The method 100 can be performed by a single datarecorder 240 (shown in FIG. 9) or can be performed using any suitablenumber of data recorders 240 to provide a redundancy of data analysisand recording capabilities. Finally, the data analysis can be partiallyor entirely performed internal to the sensors 210, 220, 230 positionedon the platform 200.

FIG. 2 shows an alternative embodiment of the method 100. Turning toFIG. 2, analyzing operational sensor data, at 110, is illustrated ascomprising establishing a profile for a platform operator, at 112.

In one embodiment, establishing the profile for an operator, at 112, cancomprise receiving a data file detailing the preferences, experience,and/or skill level of the operator in operating the platform 200 (shownin FIG. 11). The data file can be uploaded in any conventional mannerinto the platform 200, including loading the data file from a datastorage device and/or from an on-line data storage location. A newoperator may establish a profile by default settings and/or allowing anoperator to preselect certain preferences, including, for example, thesensitivity of the platform controller 310 (shown in FIG. 11). Themethod 100 advantageously enables an operator's profile to beautomatically updated over a period of time and/or a series of events.

Each individual operator can be associated with a respective operatorprofile. The operator profile can comprise sensor data from one or moreprevious events. In one embodiment, the operator profile comprisessensor data from all previous events. An event comprises a singleinteraction between the operator and the platform 200. In oneembodiment, the event can be a flight of an aircraft (not shown). Inanother embodiment, the event can comprise a flight of an unmannedaerial vehicle 200A (shown in FIG. 13). The operator profile cancomprise a subset of sensor data from the previous events. The profilecan comprise information to identify the specific operator and certainoperator selected preferences for operating the platform 200.

The operator profile quantifies operator proficiency in controlling theplatform 200. The operator profile can be established by analyzing oneor more of the following characteristics the length of time of previousevents, the distance travelled by the platform 200 during the event, thenumber of previous events, the relative smoothness of previous eventsbased on controller inputs and accelerometer data, and the accuracy ofperforming various maneuvers with the platform 200.

In one embodiment, the method 100 comprises a time and frequency-domainanalysis for determining an event time, an event distance, and/or amaximum event altitude of a selected previous event and an overallexperience level of the operator. For events related to an unmannedaerial vehicle 200A, for example, the method 100 can evaluate whetherthe operator has performed one or more selected maneuvers to determineoverall experience. Exemplary selected maneuvers can comprise tail andhead-oriented flight and/or whether circling patterns around hot spotsare relatively smooth, and/or whether in ordinary flight the flightinstrument attitudes and speeds are smoothed over.

In one embodiment, the operator profile can be stored in a data storageunit 244 (shown in FIG. 8) of the platform 200. Additionally, and/oralternatively, the operator profile can be exported to a ground station320 (shown in FIG. 11) or stored in a database (not shown) accessibleonline through the Internet.

Once the operator profile is loaded, an operating parameter isestablished, at 114, based on the operator profile. The operatingparameter can be comprised of acceleration limits, speed limits,altitude limits, or various combinations of one or more of these limits.For example, the operating parameter can be a combination of a speedlimitation at a low altitude.

Establishing an operating parameter based on the operator profile, at114, occurs prior to the operation of the platform 200 by the relevantoperator. The specific parameters to be monitored can vary based on theprofile of the operator. For example, if the operator profile indicatesthat the altitude control of the platform 200 is erratic over the pastseveral events or over a present period of time, the method 100 canestablish vertical speed (rate of climb or descent) as a parameter to bespecifically monitored. Based on the operator profile, one or more orany combination of operating parameters can be selected as operatingparameters to monitor. Additionally, establishing the operatingparameter, at 114, can determine whether the parameter will be evaluatedover a range of values, for a maximum or minimum value, and how manyoperational data points need to fall outside the established range orvalues prior to any modification of the operating parameter.

Operational data is received from the sensors, at 115. As previouslydiscussed with respect to FIG. 1, the operational data can be sampled atuniform and/or different rates. The operational data is compared to theestablished parameters to determine if the operational data is outsidethe established operating parameters, at 116. If the operational sensordata is within the operating parameter, operational data continues to bereceived, at 115, and compared with the established operating parameter,at 116. If the operational sensor data is outside the establishingoperating parameter, the performance characteristics can be altered, at120, in response to the data analysis results. For example, if the dataanalysis, at 116, determines that the operator is operating the platform200 at a high rate of speed at low altitude, a speed limitation, analtitude limitation, a range limitation or any combination thereof canbe established. In another example, if the analysis determines that theoperator control inputs are erratic resulting in operator-inducedoscillations of the platform 200, the sensitivity of the platformcontroller 310 (shown in FIG. 11) can be reduced.

In one embodiment, the method 100 advantageously can be applied tounmanned aerial vehicles (UAVs) 200A. The data processing and analysisunit 242 (shown in FIG. 7) can analyze the platform experience of theoperator and compile statistics regarding the operator's operation ofthe UAV 200A.

In one embodiment, the method 100 can employ big data analysis todetermine an operator's platform preferences. For example, by analyzingthe operational data, an operator's preferences and trends can belearned such as the mode the operator prefers to operate the platformin, the preferred altitude, and any operator created maneuvers. Themethod 100 applies to more than one operating parameter, to variousranges of the parameters, a minimum parameter value, and a maximumparameter value, etc.

Turning to FIG. 3, the method 100 is illustrated as receivingoperational sensor data, at 117, after the parameter has been adjusted,at 120. The type and/or amount of operational data received can dependon the profile of an operator, the results of the initial analysis, orsome combination thereof. The method 100 advantageously can providerepeated analysis of operational sensor data. As shown in FIG. 3, themethod 100 can comprise receiving the operational sensor data, at 117,and determining whether the operational sensor data is outside themodified operating parameters, at 118. If the operational sensor data iswithin the modified operating parameters, at 118, the process canreceive additional operational sensor data, at 117. If the operationalsensor data is outside the modified operating parameter, at 118, themethod further comprises modifying the operating performancecharacteristics again in response to the data analysis results, at 121.Following modification of the operational performance characteristics,at 121, the process can receive additional operational sensor data, at117.

Turning to FIG. 4, the process for updating an operating parameter inresponse to data analysis results, at 120, further comprises retrievingdata from prior events, at 122, analyzing prior event data to determineoperator experience, at 124, and characterizing experience based onevent data, at 126. The operator's experience is either characterized asnovice or experienced, at 126. The default characterization is novice.If the operator is characterized as a novice operator, the method 120further comprises decreasing platform 200 performance characteristics127. If the operator is characterized as an experienced operator, themethod 120 further comprises increasing platform 200 performancecharacteristics 128. In one embodiment, a novice setting can limit arange, airspeed, altitude, a point of view (POV), or acceleration of theplatform 200 (shown in FIG. 11) and an experienced operator setting canresult in increased range, airspeed, altitude, POV and acceleration of aplatform 200 up to the design performance limitations of the platform200. The novice setting can orient the forward portion (Shown in FIG.13) of the platform so it faces away from the novice platform operator.For example, when a novice operator is holding a platform controller andthe controller is rotated clockwise or counterclockwise parallel to theground about an angle, the platform rotates the same angle in the samedirection to ensure that the forward portion of the platform is facingaway from the platform controller and the rear portion of the platformis always facing toward the platform controller.

In the unmanned aerial system embodiment, the sensor data can compriseoperational and extended sensor data. The operational sensor datacomprises information from operational flight sensors 210A (shown FIG.12), such as an inertial sensor, motion sensor, position sensor, andproximity sensor. The extended data comprises information from visualsensors 220A (shown FIG. 12), and non-visual sensors (shown FIG. 12).The operational and extended sensor data may be processed initiallybecause the operational data may contain large amounts of irrelevantsensor data such as intermediate data calculated in-flight, and flightcontrol or other peripheral communication data. Data processing may alsoneed to convert operational data according to operator specifiedsettings. Such processing comprises deleting irrelevant data and/orextracting selected stored data. When the operational flight sensors210A detect conditions that would potentially precede an accident, suchas a high rate of descent, the method 100 can automatically recordvisual data such as camera images, completing image storage, in order torecreate the accident scene. Processed data can also be stored for lateranalysis in the storage equipment. The operator's flight time, flightdistance, etc., can be analyzed in the data processing and analysis unit242 (shown in FIG. 8) to determine an operator's flight experience andcompile statistics regarding the operator's degree of skill operatingthe UAV 200A, and preferred data statistics.

Turning to FIG. 5, the method 100 for data recording and analysisfurther comprises recording the sensor data in a data storage unit, at130. Although shown and described as being separate from the illustratedmethod 100, recording the sensor data in a data storage unit, at 130,can be at least partially integrated within processes 110 and/or 120.The data can be sampled at a selected rate, which can depend on thesensor 210, 220, 230 being sampled. For example, the method 100 cansample the inertial measurement unit at a rate of up to 1,000 samplesper second. By way of comparison, the GPS sensor would be sampled lessoften and in one embodiment the method would sample the GPS for positiondata at 10 samples per second.

The rate at which operational data is written to a data storage unit 244(shown in FIG. 8) can also vary depending on the specific sensor 210,220, 230. In one embodiment, the rate may range from 1 to 10 megabytes(Mb) of data recorded per second. The operational sensor data andextended data are transmitted internally from sensors coupled to theplatform 200 to the data processing and analysis unit 242 (shown in FIG.8) and saved in the data storage unit 244.

If an inertial measurement unit (IMU) sensor detects a catastrophicevent such as a rapid change in the rate of acceleration as the resultof an accident, the method 100 can vary the sampling rate of certainsensors 210, 220, 230 such as the visual sensors 220 to captureadditional information that may be useful for accident reconstruction.

In FIG. 6, the method for data recording and analysis 100 furthercomprises transferring the operational sensor data to a ground basedapparatus, at 140. Although shown and described as being separate fromthe illustrated method 100, transferring the operational sensor data toa ground based apparatus 140 can be at least partially integrated withinone or more processes 110 or 120 or 130. The operational sensor data maybe transferred any conventional manner such as wired or wireless to aground station 320 (shown in FIG. 11). For aircraft or unmanned aerialvehicles, the wired data transfer is accomplished while the platform 200(shown in FIG. 11) typically is on the ground.

Additionally, and/or alternatively, the operational and extended datacan be transferred to an operator or a plurality of other operatorswhile the platform 200 is operating. While only one operator can controlthe operation of the platform 200 at one time, a second operator cancontrol the extended data sensors. For example, for the UAV embodiment200A (shown in FIG. 12), one operator may control the flight commands(power, pitch, roll, and yaw commands), and a second operator maycommand the movement of a moveable camera attached to the UAV 200A(shown in FIG. 12).

In one embodiment, the flight data profile for the operator providingthe flight commands will determine the flight performance settings. Inanother embodiment, the extended sensor operator can develop an extendedsensor operator profile, which may save preferences for the extendedsensor operator. However, the profile of the extended sensor operatorwill not impact the flight performance characteristics of the UAV 200A.

The operational sensor data saved on the ground based apparatus 320(shown in FIG. 11) can be used to determine timing for routinemaintenance (e.g., maintenance every 100 hours of flight time) or can beuploaded to an online database for later recall in the event theplatform 200 is damaged or destroyed.

In one embodiment, the operational and extended sensor data can captureall manifestations of a platform experience that can be shared withothers at location-based service locations. The extended data that canbe shared comprises recording aerial photos/images, the geographiclocation (latitude/longitude data) as well as the photos/images height,attitude at time images are taken. The social sharing of extendedunmanned aerial vehicle data allows more operators to not only see thebeautiful images and video but to share the experience of remote, aerialviewing with others.

The transferred data can also be used to increase safety by remindingthe operator to perform periodic maintenance on the platform 200, forexample, after a predetermined number of events. The reminder isaccomplished when the operator connects to a ground station 320 (shownin FIG. 11) such as a smart phone or tablet application (App) orscheduling software on a personal computer (PC). The software willmonitor the data recorder 240 (shown in FIG. 11) and can use a dialogbox to remind the operator of required maintenance (e.g., battery ormotor service), thereby making the events involving platform 200 safer.

FIG. 7 illustrates an apparatus 240 for data recording and analysis,comprising a data processing and analysis unit 242 and an input port246I.

The apparatus 240 for data recording and analysis is built around a chipset similar to designs found in multimedia smart phones. One suchembodiment of the data processing and analysis unit 240 can be achievedusing a Reduced Instruction Set on Computers (RISC)-based computerdesign approach which uses a simplified instruction set resulting inhigher performance of executing those instructions using fewermicroprocessor cycles per instruction. One advantage to the RISC-basedcomputer design approach is reducing the number of transistors in thedesign, thereby reducing cost, heat, and power usage. These CPUcharacteristics are desirable for light, portable, battery-powereddevices. The simpler design facilitates more efficient multi-core CPUsand higher core counts at lower costs, providing improved energyefficiency. In one embodiment the design can be a 32-bit processor. Inalternate embodiment, the design can be a 64-bit processor.

Operational sensor data can be received by the data processing andanalysis unit 242 through the input port 246I that receives operationaldata from sensors 210, 220, 230 of the platform 200 through a universalserial bus (USB), controller area network (CAN), serial and/or otherstandard network connection.

FIG. 8 illustrates an apparatus for data recording and analysis 240further comprising the data processing and analysis unit 242, a datastorage unit 244, the input port 246, an output port 246O, and anexpanded data port 248.

Operational sensor data is transferred from the data recorder 240 by thedata processing and analysis unit 242 through the output port 246O. Theoutput port 246O can be a universal serial bus (USB), controller areanetwork (CAN), serial and/or other standard network connection.

The expanded data port 248 allows for use of a removable media card 249(shown in FIG. 10). The expanded data port can receive a Secure Digital(SD) card, Compact Flash (CF) card, external U-disk, and other flash orstorage equipment. The operator can specify the operational and theextended data that can be recorded on the removable media card 249. Byallowing an operator to save the expanded data on a removable media card249, operators can record images and video of their platform events forsharing with others. In one embodiment, an operator's profile can besaved on the removable media card 249 to enable the easy transfer ofprofile between various platforms 200. The expanded data port 248 cansupport removable media cards 249 with capacities from four gigabytes(GB) up to thirty-two GB or more.

Turning to FIG. 9, illustrates the data recorder 240 positioned within asystem 400 for data recording and analysis. The system 400 is shown asfurther comprising an operational sensor 210, a transceiver 250, and aplatform control system 260. Certain embodiments of the system 400 canalso comprise a visual sensor 220 and/or a non-visual sensor 230.

The system 400 for data recording and analysis can be equipped withvarious operational sensors 210 for sensing the condition of theplatform 200. Such operational sensors 210 can comprise inertialmeasurement units, accelerometers, platform control inputs, altimeters,global positioning systems, translational sensors, and outsidetemperature sensors.

Operational data sensors 210 relating to platform operations cancomprise inertial sensors such as an IMU that typically relies on acombination of accelerometers, gyroscopes, and/or magnetometers toreport on the velocity, orientation, and gravitational forces acting aselected platform 200. The IMU is used as essentially a modern-dayreplacement for a mechanical spinning-mass vertical gyroscope, in thatthe IMU is a closed system that may be used to detect attitude, motion,and sometimes some degree of location. The IMU typically uses acombination of accelerometers and angular rate sensors, commonlycomprising three accelerometers measuring three axes, and three axes ofrate gyros mounted orthogonally. Software and an associated processor,typically employing Kalman filtering, then intelligently combine theacceleration and angular rate data to give pitch/roll attitude data thatis referenced to gravity, yet is not subject to accelerations of theplatform 200. Thus the IMU provides an accurate pitch/roll attitude andheading data stream that is based purely on inertial measurement anddoes not rely on visual information, satellites, or any externaltechnological dependencies. The IMU can be connected to one of theinputs of the data recorder 240.

IMU systems are well known in the art, and descriptions of several canbe referenced in U.S. Pat. No. 4,675,820 to Smith et al., U.S. Pat. No.4,711,125 to Morrison, U.S. Pat. No. 6,725,719 to Cardarelli, and U.S.Pat. No. 7,066,004 to Kohler et al. Similar data can also be generatedusing other means such as an infrared horizon detector that use infraredsignatures in order to determine a platform's attitude in the pitch androll axes.

An accelerometer can be used to measure the vertical gravitational (G)forces. The accelerometer can be a micromachined semiconductorfabricated using Microelectromechanical Systems MEMS technology. Theaccelerometer can be mounted on the sensor board such that theaccelerometer measures G forces along one or more of the three axes ofthe platform 200. The accelerometer is used to determine if the platform200 has been subjected to severe structural stresses during an event.For an aircraft embodiment of the platform 200, stress can occur as theresult of sudden changes in altitude during turbulent conditions,unusual attitudes during a stall, spin or aerobatic maneuver and hardlandings, especially if the aircraft is being used for training.

In one embodiment, the output of the accelerometer can be a DC voltageproportional to the G force and an offset and scale factor that anadjustment circuit sets for output to ensure proper calibration. Theadjustment circuit can consist of a resistor network with a variablecomponent and some bypass capacitors. A filter consisting of aresistor-capacitor circuit can remove high frequency noise. Theresulting analog signal can represent vertical G force and can beconnected to one of the analog inputs of the flight data recorder 240.

Platform pitch, for example, can be measured using an accelerometer. Inone embodiment, the accelerometer is a micro machined semiconductordevice fabricated using MEMS technology. The device can measure pitchangle by detecting changes in the gravitational force exerted on asuspended beam which can be micro machined into the device. The outputof the accelerometer can be a DC voltage proportional to the tilt orpitch angle. A buffer can be used to prevent the accelerometer's outputfrom being loaded. A low pass filter can remove undesirable noise thatmay be present on the output of the buffer. The resulting analog signalrepresents pitch angle and can be connected to one of the analog inputsof the flight data recorder 240.

The control system 260 can, for example, control one or more flightcharacteristics, such as attitude (pitch, roll, and yaw), power, and/orvelocity, of the platform 200. The operator's input into a platformcontroller 310 may be wireless transmitted to the platform 200. Theoperator's inputs can be accomplished through the movement of one ormore control sticks on a platform controller 310 or can be a set ofcommand instructions for the platform 200 to navigate to certainprogrammed waypoints. These operator inputs can be received by theplatform transceiver 250 and transmitted to both the platform controlsystem 260 and the flight data recorder 240. These operator controlinputs can be used in the analysis of various events and recorded forlater analysis such as post-accident investigation. The operator controlinputs can be useful in determining the cause of an accident or theskill of an operator in controlling the platform 200.

The altitude of the platform 200 can be determined through varioussensors such as, but not limited to, active and passive altimetersincluding lasers, infrared, stereo vision, sonic range finders, andbarometric pressure altimeters. Similarly, additional distance-sensorsand vision sensing systems can point out of the fuselage of the platform200 to observe the movement of nearby objects to determine vertical andhorizontal movement of the platform 200 relative to a vertical objectsuch as a building or hillside.

In one embodiment, an air pressure sensor can be a semiconductor devicethat generates a DC voltage proportional to the static air pressure. Theoutput of the air pressure sensor is a DC voltage directly proportionalto air pressure. To filter any noise from the sensor, a noise decouplingfilter can be used. The resulting analog signal represents barometricaltitude and can be connected to one of the analog inputs of the flightdata recorder 240.

Through various computational operations such as integral operations ofthe acceleration sensor, the position of the platform 200 can beextrapolated from the operational data. Alternatively or additionally,the platform 200 can comprise a global positioning system (GPS) forsensing the platform's geographic position. In some embodiments, theplatform 200 can be equipped with both inertial sensors and GPS systemsthat can be used in complement with one another.

The GPS signals from the satellites are modulated using a directsequence spread spectrum with a pseudo-random code specific to eachsatellite. The GPS can comprise a signal processor that is anapplication specific integrated circuit (ASIC) that regenerates thepseudo-random code and de-spreads the GPS signal to form the basebandsignal.

The GPS receiver is capable of receiving signals from several satellitessimultaneously by having as many as twelve channels. At least sixsatellites typically are needed to determine the platform's position.The GPS estimates the arrival time of the signals from each satelliteand using the information and the known position of the satellites inorbit, the receiver's position in terms of latitude and longitude iscomputed. The resulting data can be sent out through a serial portthrough an internal bus to the data processing and analysis unit 240.

An antenna for the GPS receiver can be a printed circuit board (PCB)with the copper pattern serving as the antenna. GPS technology operatesin the microwave band of around 1.5 GHz thereby allowing antennas ofrelatively small sizes. The antenna can be mounted on the top surface ofthe fuselage of the platform 200.

A translational sensor system is a system for detecting position and/orvelocity. Beginning with images captured by a camera system, optic flow,or similar data relating to the movement of one or more objects withinthe field of view of the vision system can be gathered. Since the datacomprises both translational and rotational data coupled together, thedata preferably is decoupled through further data processing. Thedecoupling can be accomplished using measurements from the IMU sensorsystem. The IMU is one sensor for detecting attitude and/or angularrate, but other sensors can be used. Attitude and/or angular rate datacan be processed with the optic flow or similar data to generatetranslational data. Because the magnitude of the data is a function ofaltitude, the units of the data change with altitude.

To put the translational data into known units, the altitude sensor datacan be gathered and utilized to process translational data. Afterprocessing, the platform 200 position and/or velocity data is known inconstant units, and are now independent of altitude data. Platformposition and/or velocity data, platform position command data from ahuman or another computer, platform velocity command data from a humanor another computer, data from the altitude detector, and data from theattitude and/or angular rate sensor of the platform 200 can be providedto the platform control system 260. Depending on how the control system260 is set up, either one or both of these inputs may be used. From thecontrol system 260, a series of controller commands are generated inorder to cause the platform 200 to optimally perform the movementscommanded by the operator.

The decoupling process referenced above will now be described in detail.First, optic flow or similar data can be determined from the visualsensor data according to conventional optic flow and/or object trackingmethods. Next, the data regarding attitude and/or angular rate of theplatform 200 is input and optic flow or similar data corresponding tothese movements is compensated for. For example, if the platform 200 isdetected to have rolled clockwise 1.25 degrees, than 1.25 degrees isaccounted for by subtraction during the data decoupling process. Oncerotational amount is subtracted, motions detected on the visual data arenow as a result only of a change in the platform's position and anyambiguities have been removed. Hence, by tightly integrating the opticaldata with the attitude and/or angular rate data, the platform's positioncan be determined. Once position is determined, platform 200 velocitycan be determined by taking the time derivative of the position.

The processing associated with the video system will be described first.An established field of study within the computer vision community ofobject tracking within an image using computational methods alreadyexists. See U.S. Pat. No. 4,794,384 to Jackson; U.S. Pat. No. 6,384,905to Barrows, U.S. Pat. No. 6,433,780 to Gordon et al.; and U.S. Pat. No.6,507,661 to Roy. In one embodiment, the perceived visual motion ofobjects as an observer moves relative to those objects allows anoperator to judge how close he is to certain objects and his movementrelative to them. For instance, to an operator, an object slowly growinglarger and larger, but not moving to one side of the operator's visioncould be understood by the operator to be moving directly towards theobserver. In the one embodiment, the central processing unit 242 cantrack all “objects” or landmarks within a video image. The trackedobjects should all move with approximately the same speed and directionwhen the camera is pointed toward the ground and the landmarks withinthe image are all on the ground. A correlation between the movements ofthe landmarks within the image is detected by a processor 242. Theprocessing could reject or ignore any landmarks that do not fit thecorrelation, such as a bird flying closely under the platform 200.Various software methods could be used to determine the relativemovement as detected by the camera. In addition, various softwaremethods can provide varying degrees of robustness and rejection of falsemovements.

The translational data computation system can employ feature selection,a means of object tracking, whereby the best features from a contrastproperties perspective are tracked. There is no need for the imagingsystem to correctly identify and label objects such as trees or cars orpainted lines on the ground. The translational data computation systemmerely has to know the object observed (in the case of a tree, a tallgreen object) is something to be tracked through subsequent imageframes. Knowing the identity of the object is not necessary tounderstand the platform's movement relative to the object. The objecttracking feature is advantageous because object tracking can beimplemented using typical inexpensive processors and computer powercurrently available. The method of object tracking also means that theterrain below the platform 200 and the obstacles near the platform 200do not have to be known or defined in advance. In an alternativeembodiment, the system can identify and track one or more recognizableobjects if an operator desires the platform 200 to move relative tospecific object(s) within the vision system's field of view.

The disclosed translational data computation method can determine amovement vector of an image in the video sequence analyzed by the system400. From the analysis of the video sequence, the computer still cannotdetermine the amount, if any, of translational movement of the platform200. The complications and solution for each are herein described.

Rotational movement of the platform 200 results in a similar videosequence as translational movement. Thus, trying to operate the platform200 purely by a visual data steam would result in operator controlinputs being made on ambiguous data, which would likely prove disastrousif the platform 200 encounters any substantial attitude changes.However, by decoupling the rotational movement from the translationalmovement in the video sequence, the ambiguous data becomes certain. Thedecoupling occurs by using a properly tuned IMU. An IMU can output adata stream of accurate pitch/roll/yaw attitude information that isdetermined purely on inertial measurements. The data stream outputtedfrom the IMU is used to determine how much of the movement observed inthe video sequence is due to rotational platform changes (attitudechange) versus how much of the movement is due to translational (e.g.,position change).

The degree of rotation detected by both the IMU and the vision systemconstitutes the Y-axis and the sample number constitutes the X-axis. Asthousands of sensor data samples are taken every second, just a fewseconds of sensor data results in many thousands of data points. Thesensor data will be subtracted from each other to remove the rotationalcomponent from the visual signal and thus obtain translational position.Subtracting the one signal from the other here results in zerotranslational movement.

Regardless of the altitude of a platform 200 equipped with such asystem, rotational movements would appear similarly in the videosequence because the camera is being swept a certain amount of degreesper second over the landscape. Thus, the video sequence can be decoupledby taking the pitch/roll attitude of the platform 200, multiplying thisby a factor to equalize pitch/roll data and video data, and thensubtracting from this amount the perceived displacement of the camerafrom the video sequence.

In one embodiment of the disclosed system 400, an outside airtemperature can be measured using a solid state temperature sensor. Theoutside air temperature sensor can comprise an integrated circuit thatgenerates a DC voltage that is directly proportional to the temperatureof the air surrounding the temperature sensor. Two wires connect thesensor to a differential amplifier that provides some gain and a lowimpedance output. The resulting analog signal represents outside airtemperature and can be connected to one of the analog inputs of the datarecorder 240.

In one embodiment of the system 400, a visual sensor 220 can be used forcapturing visual flight data as shown in FIG. 9. The visual sensor 220advantageously is lightweight and able to provide a high-frequency datafeed to other components of the data recording and analysis system 400to facilitate real-time or near real time collection and presentation ofvisual data. The visual sensor 220 can comprise a variety ofconventional cameras for image and video acquisition.

Additionally and/or alternatively, the platform 200 can be equipped withat least one non-visual sensor 230 for collecting non-visual datarelating to sound, temperature, pressure, humidity, precipitation, windspeed and direction, and/or other environmental factors that are noteasily captured visually. Exemplary instruments for non-visual datacollection can comprise, but are not limited to, electro-opticalsensors, thermal/infrared sensors, color or monochrome sensors,multi-spectral imaging sensors, spectrophotometers, spectrometers,thermometers, illuminometers, microphones/sonic transducers, pressuresensors, altitude sensors, flow sensors, humidity sensors, precipitationsensors, wind speed sensors, wind direction sensors, anemometers,optical rain sensors, and/or other suitable devices for data collection.

FIG. 9 also illustrates that the system 400 can comprise the datarecorder 240. The data recorder 240 provides for the collection,analysis, and/or storage of operational platform data in addition toextended data. The data recorder 240 is discussed in more detail abovewith referenced to FIG. 7 and FIG. 8. In one embodiment, the datarecorder 240 can be mounted in a housing (not shown) formed from ametal, such as stainless steel. The housing is serves as the first layerof protection of the data recorder against impact, fire and/or water. Inother embodiments, the housing can be made from one or more othersuitable materials such as aluminum, fiberglass, composite fibermaterials or other protective materials. A thermal insulation made ofsilica-based material protects the data recorder 240 from the heat,serving as the second layer of protection against fire.

One such embodiment of the data recorder is a flight data recorder 240A(shown in FIG. 12) that can be used on passenger aircraft. Flight datarecorders 240A can record predetermined flight parameters, such as thecontrol and actuator positions, engine information, geographic position,and/or time of day. For passenger aircraft, the FAA requires thateighty-eight parameters be monitored and recorded, but some systemsmonitor many more parameters. Generally, each parameter is recorded afew times per second, though some flight data recorders 240A store“bursts” of data at a much higher frequency if the data begins to changequickly. Most flight data recorders 240A can record approximatelyseventeen to twenty-five hours worth of data in a continuous loop. U.S.federal regulations do not currently require flight data recorders 240Afor unmanned vehicles 200A (shown in FIG. 12).

As shown in FIG. 9, the data recording and analysis system 400incorporates a transceiver 250 for the transmission and reception ofdata between the platform 200 and ground based systems 300, including aplatform controller 310 and a ground station 320. The transceiver 250can receive platform control commands from the platform controller 310and route the commands to the platform control system 260 in anyconventional manner, such as wired or wireless. The transceiver 250 cantransmit operational sensor data and extended sensor data to the groundstation 320.

A transceiver 250 section consists of several elements including a radiofrequency (RF) filter that allows only the desired signal band to passthrough. The transceiver 250 also has a RF front end which is anintegrated circuit that performs the function of down converting the RFsignal to the intermediate frequency (IF) signal, amplifying the IFsignal, filtering the signal using the IF filter and converting thesignal to two digital components the sign and the magnitude, usingon-chip analog-to-digital converters. A phase locked loop filter is usedfor the down converters oscillator built into the RF front end togetherwith reference crystal which serves as a time base. The gain of the RFfront end IF amplifier can be controlled by the automatic gain control(AGC) signal.

The operating frequency of the data transceiver can be in the microwaverange, 5.728 GHz-5.85 GHz. The data transceiver antenna receives andtransmits radio signals. The impedance can be matched to the rest of thecircuit using an antenna matching network which consists of aninductor/capacitor network. The transceiver can be an applicationspecific integrated circuit that performs the function of receiving andtransmitting the microwave signals. The power output can be in the rangeof several milliwatts since the transceiver is designed to work overshort distances, namely, 300 to 500 meters.

In one embodiment, the platform 200 can be equipped to communicatewirelessly with one or more other system components, such as theplatform controller 310 and/or the ground station 320, of the datarecording and analysis system 400. The platform 200 of the datarecording and analysis system 400 can operate as a communicationsendpoint, such as a cell phone, within a wireless communicationsnetwork. Thus, any conventional wireless communication protocolappropriate for communications endpoints can facilitate communicationsbetween the similar platforms 200, the platform controller 310, and/orany other components of the data recording and analysis system 400. Forexample, the platform 200 can establish a data uplink and/or downlinkchannels with the controller 310 or ground station 320.

The transceiver 250 can also operate using any category of conventionalwireless communications, for example, radio, Wireless Fidelity (WiFi),cellular, satellite, and broadcasting. Exemplary suitable wirelesscommunication technologies comprise, but are not limited to, GlobalSystem for Mobile Communications (GSM), General Packet Radio Service(GPRS), Code Division Multiple Access (CDMA), Wideband CDMA (W-CDMA),CDMA2000, IMT Single Carrier, Enhanced Data Rates for GSM Evolution(EDGE), Long-Term Evolution (LTE), LTE Advanced, Time-Division LTE(TD-LTE), High Performance Radio Local Area Network (HiperLAN), HighPerformance Radio Wide Area Network (HiperWAN), High Performance RadioMetropolitan Area Network (HiperMAN), Local Multipoint DistributionService (LMDS), Worldwide Interoperability for Microwave Access (WiMAX),ZigBee, Bluetooth, Flash Orthogonal Frequency-Division Multiplexing(Flash-OFDM), High Capacity Spatial Division Multiple Access (HC-SDMA),iBurst, Universal Mobile Telecommunications System (UMTS), UMTSTime-Division Duplexing (UMTS-TDD), Evolved High Speed Packet Access(HSPA+), Time Division Synchronous Code Division Multiple Access(TD-SCDMA), Evolution-Data Optimized (EV-DO), Digital Enhanced CordlessTelecommunications (DECT) and others.

In certain embodiments, the platform 200 and subsystems of the datarecording and analysis system 400 can communicate via third or fourthgeneration wireless 3G or 4G mobile telecommunications technologies. The3G and 4G technologies are based on standards for mobiletelecommunications that comply with international specificationspromulgated by the International Telecommunications Union (ITU). The 3Gand 4G technologies provide information transfer rates ranging from 200kilobits per second up to several gigabits per second, making thembroadly suitable for transmission of high-resolution images and videothat use large bandwidth. 3G technologies generally are those that meetthe International Mobile Telecommunications 2000 (IMT-2000) standardsfor reliability and data transfer rates. Common commercial 3Gtechnologies comprise, for example, systems and radio interfaces basedon spread spectrum radio transmission technology, such as the UMTSsystem standardized by the 3rd Generation Partnership Project (3GPP),the W-CDMA radio interface, the TD-SCDMA radio interface offered inChina, the HSPA+ UMTS release, the CDMA2000 system, and EV-DO. Inaddition, other technologies such as EDGE, DECT, and Mobile WiMAX alsofulfill IMT-2000 and thus are also approved as 3G standards by the ITU.Accordingly, the term “3G” as used herein comprise, but is not limitedto, any IMT-2000 compliant technology, including those discussed herein.

In contrast, 4G technologies are generally understood to be those thatcomply with the International Mobile Telecommunications Advanced(IMT-Advanced) specification, requiring peak speed requirements at 100megabits per second for high mobility communications and one gigabit persecond for low mobility communications. As of October 2010, ITU-approved4G standards comprise LTE Advanced and WirelessMAN-Advanced (e.g., IEEE802.16m). However, many commercial carriers advertise 4G services thatmay not fully comply with the IMT-Advanced specification, such as LTE,Mobile WiMAX, and TD-LTE. Accordingly, as used herein, the term “4G”comprises, but is not limited to, these latter technologies such as LTE,Mobile WiMAX, and TD-LTE, as well as those which are IMT-Advancedcompliant, including those technologies described herein.

In other embodiments, the platform 200 can use fifth generation (5G)mobile telecommunications networks to facilitate communications betweenthe relevant subsystems of data recording and analysis system 400 andmethods. 5G is the next phase of mobile telecommunications standardsbeyond current 4G/IMT-Advanced standards.

In some embodiments, the wireless communications used by the subsystemsof the present system can be encrypted, as may be advantageous forsecure communication in the data recording and analysis system 400.Suitable encryption methods comprise, but are not limited to, internetkey exchange, Internet Protocol Security (IPsec), Kerberos,point-to-point protocol, transport layer security, SSID hiding, MAC IDfiltering, Static IP addressing, 802.11 security, Wired EquivalentPrivacy (WEP), Wi-Fi Protected Access (WPA), WPA2, Temporal KeyIntegrity Protocol (TKIP), Extensible Authentication Protocol (EAP),Lightweight Extensible Authentication Protocol (LEAP), ProtectedExtensible Authentication Protocol (PEAP), and other commerciallyavailable encryption techniques.

Thus, existing wireless technologies for use by currenttelecommunications endpoints can be readily adapted for use by theplatform 200. For example, by outfitting each platform 200 with awireless card like those used for mobile phones, or other suitablewireless communications hardware, the platform 200 can easily beintegrated into existing networks. Alternatively, and/or additionally,proprietary communications hardware can be used as needed.

As shown in FIG. 9, the data recording and analysis system 400 cancomprise a platform control system 260. The platform control system 260can receive control commands from a platform controller 310 via thetransceiver or from the data recorder 240 based on analysis ofoperational data as previously discussed.

The data recording and analysis system 400 can be incorporated intovarious types of platforms 200 including an unmanned aerial vehicle200A. One popular type of UAV 200A, for example, is an aerial rotorcraftthat is propelled by multiple rotors. A rotorcraft that has four rotorsand is known as a quadcopter, quadrotor helicopter, or quad rotor. FIG.13 illustrates a quadcopter. Such a design provides the UAV 200A with ahigh range of motion, providing for vertical takeoff and landing as wellas the ability to hover in mid-air for still aerial image acquisition.Exemplary quadcopters suitable for use with the data recording andanalysis system 400 comprise numerous models currently availablecommercially. Various other types of unmanned aerial vehicles 200A aresuitable for use with the data recording and analysis system 400,including other rotor designs including a single rotor helicopter, dualrotor, trirotor, hexarotor, and octorotor designs. Fixed wing and hybridrotorcraft-fixed wing UAV 200A can also be used.

FIG. 13 provides an illustration of a quadrotor with four medium-sizedrotors mounted with their thrust vectors pointing in the same downwarddirection. The rotor/fans are positioned at respective corners of theUAV 200A and that each rotor/fan provides lift to support the relevantcorner. The fans can be arranged in any suitable manner, including adiamond configuration (with one fan in the front, one in the rear, oneon the right, and one on the left) and/or a rectangle configuration (twofans on the left and two fans on the right). The total thrust of eachrotor can be controlled in any conventional manner, including by varyingthe rotational speed (RPM) of the rotor and/or by varying the pitch ofeach propeller. The quadcopter comprises a mixing unit comprising acomputer (not shown) that reads pitch and roll commands from the controlsystem 260 that outputs individual thrust commands to each of the fourpropellers. For example, if the control system 260 commands “bankright,” then the mixing unit can command the fan(s) on the left toprovide increased thrust (either by speeding up or increasing pitch),and the fan(s) on the right side can provide less thrust (either byslowing down or by decreasing pitch). Similarly, a pitch forward commandcan result in more thrust from the rear fan(s) and less thrust from thefront fan(s). A yaw command would cause the two clockwise spinning fansto speed up and the two counterclockwise fans to slow down, assuming twofans run in one direction and two run in the other.

One approach for controlling unmanned vertical take-off and landing(VTOL) aircraft is to make the UAV 200A (shown in FIG. 12) remotecontrolled from an external position using a remote controller. Allpiloting commands are transmitted wirelessly to the UAV 200A, and hence,the remote operator can control the UAV 200A from the remote location.The remote operator can monitor the UAV 200A either visually by using aclear line-of-site, observing video displays, sensors, or somecombination thereof. By mounting one or more remotely viewable videocameras on the UAV 200A, a remotely located operator can gain some senseof UAV 200A position and velocity. In any case, it is advantageous tohave a direct visual line of site for operations in close proximity tothe ground such as during take-off and landing operations so that theoperator can gain direct visual cues from the aircraft apart from thevideo system. Thus, while the disclosed method of controlling a UAV 200Ahas been used in fixed-wing aircraft, remote aircraft control has thedrawback of requiring a high level of operator skill and interventionwhen applied to the VTOL UAV.

Another approach used to control unmanned VTOL aircraft combines some ofthe techniques described above with an on-board stability controlsystems and “autopilot” system. The autopilot system can use an InertialMeasurement Unit (IMU) to enable the UAV 200A to make small adjustmentsto maintain level flight and/or hover. Although this disclosed method ofcontrol does provide rotational sensory information, it does not provideany translational information. Hence, the system will not account forthe difference between a hovering aircraft and one that is flying at ahigh speed, since both UAVs 200A may be level with respect to the Earth.The use of an autopilot system with a remote control can make the UAV200A easier to control than using a remote control only approach, butessentially all the same drawbacks still apply.

A third approach to controlling a UAV 200A is similar to the second,only with the addition of an onboard GPS capability to control theflight path of the UAV 200A. Using this approach an operator wouldprogram several waypoints into the UAV 200A flight computer. Then thecomputer would control the UAV 200A to fly the specified path. Typicallythe flight path would take place far from obstacles due to the lowresolution of the system. A human operator would typically be requiredfor take-off and landing the UAV, unless a very large open landing areawas available and the aircraft was capable of handling a less thansmooth landings. However, with a GPS commanded autopilot system,loitering near the ground, buildings, or other points of interestremotely is typically not a feasible option.

Although shown in FIG. 9 and described as including one operationalsensor for purposes of illustration only, the platform can comprise anysuitable number of operational sensors 210. Exemplary suitable sensorscan comprise at least one of accelerometers, gyroscopes, inertialmeasurement units, altimeters, global positioning sensors, air pressuresensor and air temperature sensors. The operational sensors 210 can beuniform and/or differ for a selected platform 200, and/or the numberand/or combination of sensors can be uniform and/or different for eachplatform 200 in a fleet of platforms 200.

FIG. 10 illustrates another apparatus for data recording and analysiscomprising a data processing and analysis unit 242, a data storage unit244, a network port 245, an input port 246I, an output port 246O, and anexpanded data port 248 and a removable data storage card 249.

The data recorder 240 also incorporates a data storage unit 244. Thedata storage unit 244 can incorporate a separate set of protectiveenclosures. The separate set of protective enclosures can help increasethe probability that the recorded data stored in the data storage unit244 can be recovered even if most of the internal parts of the datarecorder 240 are damaged. Use of the separate set of protectiveenclosures for the data store unit 244 provides a further advantage ofreducing the overall enclosure cost since the degree of protection canbe concentrated on the data storage unit 244, which is a much smallercomponent when compared to the data recorder 240.

The data storage unit 244 can provide internal redundancy by includingmultiple memory modules. Each memory module is capable of recording atleast four gigabytes (GB) of data. The memory modules advantageous useflash memory that is electrically erasable and programmable memorydevices that can be written to and/or indefinitely retain contents evenin the absence of power. The nonvolatile flash memory can interface withthe data processing and analysis unit 240 in any conventional manner,including through a serial port and/or parallel port.

Data retrieval is normally accomplished when the platform 200 is notmoving on the ground. Although embodiments of the unmanned aerial systemallow the export of operational and extended data while in flight. Aportable personal computer (PC) can function as the ground station 320.A portable electronic device such as a smart phone or tablet can alsofunction as a ground station 320. Once transferred to a ground station320, operational or extended platform data can be used forexperimentation, post-accident data analysis, or social sharing on alocation based social network.

The data recorder 240 has the capacity to store data from alloperational 210, visual 220 and non-visual sensors 230. In oneembodiment, the operator can select the sensors the data recorder 240will record to the data storage unit 244. The data storage unit 244 canstore several events or flights worth of operational sensor information,visual and non-visual data. The recorded data can be stored in the datamodules 244 in any conventional format, including a proprietary format.

The operational and extended data stored allows for various post-eventanalyses including social use of the data. The operational and extendeddata can be used to determine the operator's platform 200 experience. Anoperator can create a profile and save the operational data from his orher events with the platform 200.

Electrical power for the flight data recorder 240 can be derived fromthe platform's electrical supply and typically can be in the range of11.1-12.6 volts DC. The platform power to the controller module, GPSreceiver module, certain circuits of sensor and signal conditioningmodule, back-up memory module and radio frequency data transceiver 250.During a power failure during operation, the data recorder 240 cancontinue to operate through the use of an optional back-up battery (notshown).

The network port 245 allows for a common interface for both the inputport 246I and output port 246O. Data can be transferred through a dataoutput port 246O through a universal serial bus (USB), controller areanetwork (CAN), serial and other standard network connections. Thenetwork port 245 receives data from the plurality of sensors positionedon the platform 200 through the data input port 246I as previouslydiscussed. Sensor data enters through the data input port 246I and istransferred to the data processing and analysis unit 242. Data istransmitted from the UAV 210A (shown in FIG. 12) to the ground station320 through the output port 246O.

FIG. 11 illustrates another embodiment of the system 400 for dataanalysis comprising an operational sensor 210, a visual sensor 220, anon-visual sensor 230, a data recorder 240, a platform control system250, a transceiver 260, a platform controller 310 and a ground station320. Each of these elements can be operated in the manner as discussedabove and as illustrated in FIGS. 1-10

Although shown in FIG. 11 and described as including one ground stationfor purposes of illustration only, the platform can comprise anysuitable number of ground stations 320. Exemplary suitable groundstations can comprise at least one of a smart phone, a tablet, or apersonal computer. The ground station 320 can be uniform and/or differfor a selected platform 200, and/or the number and/or combination ofground stations can be uniform and/or different for each platform 200 ina fleet of platforms 200.

FIG. 12 illustrates another embodiment of the system 400 for dataanalysis, wherein the platform 200 is an unmanned aerial vehicle 200A.The system 400 comprises, an operational sensor 210A, a visual sensor220A, a non-visual sensor 230A, a flight data recorder 240, a UAVtransceiver 250, an UAV control system 260, a UAV controller 310 and aUAV ground station 320A.

In some embodiments, the ground station 320 can be located on a vacantland area where the UAV 200A is allowed to land safely until the UAV200A can be manually located, recharged, and/or maintained for furtheroperation. In other embodiments, the ground station 320A can comprisecertain support systems, such as a recharging station for rechargingbatteries that power the UAV 200A. In other embodiments where the UAV200A is powered by power sources other than electricity, the groundstation 320A can similarly comprise other suitable support systems forreplenishing the power supply of the UAV 200A. Such recharging stationsand other power stations preferably allow automatic docking of the UAVs200A so as to enable recharging/repowering without human intervention.In some embodiments, the support systems at ground station 320A arelightweight and portable so as to be unobtrusive and easily moved whenrelocation of the ground station 320 is desired.

In some embodiments, each ground station 320A is configured toaccommodate a single UAV 200A. In other embodiments, each ground station320 is configured to simultaneously accommodate multiple UAVs 200A.

An exemplary software interface allows operator control of a single UAV200A using a mobile device over a Wi-Fi network. Various input andoutput functions can be implemented via a exemplary operator interfacethat comprises, without limitation, a navigation between menus andmodes, camera controls, flight attitude and radar functions, flightparameters, wireless signal intensity, UAV power level, UAV GPS status,memory status, memory slot status, camera shutter button, camera recordbutton, camera settings, and flight parameters. In one embodiment, bothvisual and non-visual data are presented visually presentation of visualdata is in the form of the images displayed on-screen, whilepresentation of non-visual data is in the form of parameters that arealso visually displayed.

Generally, the data is transmitted from the data recorder 240 to aground station 320. For example you can have a portable electronicdevice with an application installed on it to display the data.Operational and extended data can also be transmitted to other types ofportable electronic devices such as smart phones while the UAV 200A isin flight.

Although shown in FIG. 12 and described as including one UAV groundstation 320A for purposes of illustration only, the unmanned aerialvehicle 200A can comprise any number of UAV ground stations 320A.Exemplary suitable UAV ground stations 320A can comprise at least one ofsmart phone, tablet, or personal computer. The UAV ground station 320Acan be uniform and/or differ for a selected unmanned aerial vehicle200A, and/or the number and/or combination of UAV ground stations 320Acan be uniform and/or different for each UAV 200A in a fleet of UAVs.

The disclosed embodiments are susceptible to various modifications andalternative forms, and specific examples thereof have been shown by wayof example in the drawings and are herein described in detail. It shouldbe understood, however, that the disclosed embodiments are not to belimited to the particular forms or methods disclosed, but to thecontrary, the disclosed embodiments are to cover all modifications,equivalents, and alternatives.

1-30. (canceled)
 31. A data analyzing method comprising: analyzing, by aprocessor, operational data of a platform operator of a moving platformcollected by a sensor mounted on the moving platform; and updating, bythe processor, an operating parameter of the moving platform in responseto analyzing the recorded operational data, including: retrievingselected operational data from a plurality of prior events; analyzingthe selected operational data to determine an experience level of theplatform operator; characterizing the experience level of the platformoperator based on analyzing the selected operational data; and either:increasing platform performance characteristics of the moving platformwhen the moving platform is operated by an experienced platform operatoras determined by characterizing the experience level of the platformoperator; decreasing the platform performance characteristics when themoving platform is operated by a novice platform operator as determinedby characterizing the experience level of the platform operator; or acombination thereof.
 32. The method of claim 11, further comprising:enabling different level functions of the moving platform based oncharacterizing the experience level of the platform operator.
 33. Themethod of claim 32, wherein enabling the different level functionscomprises limiting at least one of a height, a distance, or a velocityof the moving platform.
 34. The method of claim 31, wherein analyzingthe operational data further includes: establishing an operator profilefor the platform operator; establishing an initial value for theoperating parameter based on the operator profile; and determiningwhether the operational data is outside the initial value of theoperating parameter.
 35. The method of claim 34, wherein updating theoperating parameter includes: altering the operating parameter of themoving platform if the operational data is outside the initial value;and maintaining the operating parameter of the moving platform if theoperational data is within the initial value.
 36. The method of claim31, wherein analyzing the operational data includes analyzing selectedoperational data associated with at least one of a geographic positionof the moving platform, an altitude of the moving platform, anacceleration of the moving platform, a speed of the moving platform, ora controller input received by the moving platform.
 37. The method ofclaim 31, wherein analyzing the operational data comprises at least oneof recording still images or video from a camera mourned on the movingplatform, recording audio from a microphone mounted on the movingplatform, or determining a cause of an accident involving the movingplatform.
 38. The method of claim 31, further comprising recording theoperational data in a data storage unit.
 39. The method of claim 31,further comprising transmitting the operational data to a ground basedapparatus during operation of the moving platform.
 40. A data analyzingsystem comprising: a sensor mounted on a moving platform and configuredto collect operational data of a platform operator of the movingplatform; and a data recorder comprising: a data storage configured tostore the operational data; and a processor configured to: analyze theoperational data, and update an operating parameter of the movingplatform in response to analyzing the operational data, including:retrieving selected operational data from a plurality of prior events;analyzing the selected operational data to determine an experience levelof the platform operator; characterizing the experience level of theplatform operator based on analyzing the selected operational data; andeither:  increasing platform performance characteristics of the movingplatform when the moving platform is operated by an experienced platformoperator as determined by characterizing the experience level of theplatform operator;  decreasing the platform performance characteristicswhen the moving platform is operated by a novice platform operator asdetermined by characterizing the experience level of the platformoperator; or  a combination thereof.
 41. The system of claim 40, whereinthe processor is further configured to enable different level functionsof the moving platform based on characterizing the experience level ofthe platform operator.
 42. The system of claim 41, wherein the processoris further configured to limit at least one of a height, a distance, ora velocity of the moving platform.
 43. The system of claim 40, whereinthe processor is further configured to: establish an operator profilefor the platform operator; establish an initial value for the operatingparameter based on the operator profile; and determine whether theoperational data is outside the initial value of the operatingparameter.
 44. The system of claim 43, wherein the processor is furtherconfigured to: alter the operating parameter of the moving platform ifthe operational data is outside the initial value; and maintain theoperating parameter of the moving platform if the operational data iswithin the initial value.
 45. The system of claim 40, wherein theprocessor is further configured to analyze selected operational dataassociated with at least one of a geographic position of the movingplatform, an altitude of the moving platform, an acceleration of themoving platform, a speed of the moving platform, or a controller inputreceived by the moving platform.
 46. The system of claim 40, wherein theprocessor is further configured to determine a cause of an accidentinvolving the moving platform.
 47. The system of claim 40, furthercomprising: an imaging device mounted on the moving platform andconfigured to capture still images or video, wherein the processor isfurther configured to store the still images or video to the datastorage unit.
 48. The system of claim 40, further comprising: amicrophone mounted on the moving platform and configured to recordaudio, wherein the processor is further configured to store the audio tothe data storage unit.
 49. The system of claim 40, further comprising: atransmitter coupled to the data recorder and configured to transmit theoperational data to a ground station.
 50. A data recorder comprising: adata storage unit configured to store operational data of a platformoperator of a moving platform collected by a sensor positioned on themoving platform; and a processor configured to: analyze the operationaldata, and update an operating parameter of the moving platform inresponse to analyzing the operational data, including: retrievingselected operational data from a plurality of prior events; analyzingthe selected operational data to determine an experience level of theplatform operator; characterizing the experience level of the platformoperator based on analyzing the selected operational data; and either:increasing platform performance characteristics of the moving platformwhen the moving platform is operated by an experienced platform operatoras determined by characterizing the experience level of the platformoperator; decreasing the platform performance characteristics when themoving platform is operated by a novice platform operator as determinedby characterizing the experience level of the platform operator; or acombination thereof.